Training data from: Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and over half of wetlands; under a 2020 White House rule, it protects under half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems. , This dataset contains data used to train the models., , # Training data from: Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates

This dataset contains data used to train the models in [Greenhill et al. (2023)](https://doi.org/10.1126/science.adi3794). All data are publicly available and can be accessed either through Google Earth Engine or directly from the data providers, as described in Table S3 of the Supplementary Material. In addition, we are providing access to the full set of pre-processed inputs for model training via this repository. We are also providing access to a subset of the data used for prediction, as well as all data needed for reproducing the results of the paper, in another Dryad repository: . All code written for the project is available at .

## Description of the data and file structure

The files here include:

* Trained models, saved in PyTorch Checkpoint format: `wotus_model.pth.tar`, `resource_type_model.pth.tar`, `cowardin_code_model.pth.tar`, `ajd_model.pth.tar`.
* ...